多策略合成平衡优化器和应用程序
Quandang Sun1,2, Xinyu Zhang3, Ruixia Jin4
1Engineering Lab of Intelligence Business & Internet of Things, Xinxiang, Henan, China.
PeerJ. Computer science
|January 23, 2024
概括
本研究介绍了更新合成平衡优化器 (MS-EO) 的多策略,以解决原始平衡优化器 (EO) 的局限性. 增强的MS-EO算法在复杂的优化任务和功能选择中表现出卓越的性能.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 机器学习 机器学习
背景情况:
- 在复杂的优化中,平衡优化器 (EO) 面临着搜索能力,计算复杂性和可操作性的挑战.
- 现有的算法需要改进,以便在苛刻的计算任务中提高性能.
研究的目的:
- 提出一个改进的平衡优化器 (EO) 算法,称为更新合成EO的多策略 (MS-EO).
- 为了提高搜索能力,减少计算复杂性,并提高EO算法的可操作性.
主要方法:
- 引入了一种简化更新策略,以提高可操作性和减少复杂性.
- 整合了信息共享战略,并进行了动态调整,以改善勘探.
- 整合迁移和黄金部分战略,以增强搜索能力.
- 实施精英学习策略,以加强开发能力,平衡勘探和开发.
主要成果:
- 在CEC2013和CEC2017复杂函数测试集上,MS-EO显著超过了原来的EO和其他最先进的算法.
- 与现有方法相比,证明了优越的搜索能力,运行速度和可操作性.
- 在多个数据集中展示了功能选择任务的优势.
结论:
- 拟议的MS-EO有效平衡勘探和开采,比标准EO提供了显著的改进.
- 对于复杂的优化问题和特征选择应用程序,MS-EO提供了强大而高效的替代方案.
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